Triclustering Georeferenced Time Series for Analyzing Patterns of Intra-Annual Variability in Temperature

Xiaojing Wu (Corresponding Author), Raul Zurita-Milla (Corresponding Author), Emma Izquierdo Verdiguier (Corresponding Author), Menno-Jan Kraak (Corresponding Author)

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
13 Downloads (Pure)

Abstract

Clustering is often used to explore patterns in georeferenced time series (GTS). Most clustering studies, however, only analyze GTS from one or two dimension(s) and are not capable of the simultaneous analysis of the data from three dimensions: spatial, temporal, and any third (e.g., attribute) dimension. Here we develop a novel clustering algorithm called the Bregman cuboid average triclustering algorithm with I-divergence (BCAT_I), which enables the complete partitional analysis of 3D GTS. BCAT_I simultaneously groups the data along its dimensions to form regular triclusters. These triclusters are subsequently refined using k means to fully capture spatiotemporal patterns in the data. By applying BCAT_I to time series of daily average temperature in The Netherlands (twenty-eight weather stations from 1992 to 2011), we identified the refined triclusters with similar temperature values along the spatial dimension (weather stations that represent locations) and two nested temporal dimensions (year and day). Geovisualization techniques were then used to display the patterns of intra-annual variability in temperature. Our results show that in the last two thirds of the study period, there is an intense variability of spring and winter temperatures in the northeast and center of The Netherlands. For the same period, an intense variability of spring temperatures is also visible in the southeast of the country. Our results also show that summer temperatures are homogenous across the country for most of the study period. This particular application demonstrates that BCAT_I enables a complete analysis of 3D GTS and, as such, it contributes to a better understanding of complex patterns in spatiotemporal data.

Original languageEnglish
Pages (from-to)71-87
Number of pages17
JournalAnnals of the American Association of Geographers
Volume108
Issue number1
DOIs
Publication statusPublished - 2 Jan 2018

Fingerprint

time series
divergence
temperature
weather station
Netherlands
simultaneous analysis
winter
summer
Values
Group
analysis

Keywords

  • ITC-ISI-JOURNAL-ARTICLE

Cite this

@article{40571ecbcac640adb4cce0681461e09a,
title = "Triclustering Georeferenced Time Series for Analyzing Patterns of Intra-Annual Variability in Temperature",
abstract = "Clustering is often used to explore patterns in georeferenced time series (GTS). Most clustering studies, however, only analyze GTS from one or two dimension(s) and are not capable of the simultaneous analysis of the data from three dimensions: spatial, temporal, and any third (e.g., attribute) dimension. Here we develop a novel clustering algorithm called the Bregman cuboid average triclustering algorithm with I-divergence (BCAT_I), which enables the complete partitional analysis of 3D GTS. BCAT_I simultaneously groups the data along its dimensions to form regular triclusters. These triclusters are subsequently refined using k means to fully capture spatiotemporal patterns in the data. By applying BCAT_I to time series of daily average temperature in The Netherlands (twenty-eight weather stations from 1992 to 2011), we identified the refined triclusters with similar temperature values along the spatial dimension (weather stations that represent locations) and two nested temporal dimensions (year and day). Geovisualization techniques were then used to display the patterns of intra-annual variability in temperature. Our results show that in the last two thirds of the study period, there is an intense variability of spring and winter temperatures in the northeast and center of The Netherlands. For the same period, an intense variability of spring temperatures is also visible in the southeast of the country. Our results also show that summer temperatures are homogenous across the country for most of the study period. This particular application demonstrates that BCAT_I enables a complete analysis of 3D GTS and, as such, it contributes to a better understanding of complex patterns in spatiotemporal data.",
keywords = "ITC-ISI-JOURNAL-ARTICLE",
author = "Xiaojing Wu and Raul Zurita-Milla and {Izquierdo Verdiguier}, Emma and Menno-Jan Kraak",
year = "2018",
month = "1",
day = "2",
doi = "10.1080/24694452.2017.1325725",
language = "English",
volume = "108",
pages = "71--87",
journal = "Annals of the American Association of Geographers",
issn = "2469-4452",
publisher = "Taylor & Francis",
number = "1",

}

Triclustering Georeferenced Time Series for Analyzing Patterns of Intra-Annual Variability in Temperature. / Wu, Xiaojing (Corresponding Author); Zurita-Milla, Raul (Corresponding Author); Izquierdo Verdiguier, Emma (Corresponding Author); Kraak, Menno-Jan (Corresponding Author).

In: Annals of the American Association of Geographers, Vol. 108, No. 1, 02.01.2018, p. 71-87.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Triclustering Georeferenced Time Series for Analyzing Patterns of Intra-Annual Variability in Temperature

AU - Wu, Xiaojing

AU - Zurita-Milla, Raul

AU - Izquierdo Verdiguier, Emma

AU - Kraak, Menno-Jan

PY - 2018/1/2

Y1 - 2018/1/2

N2 - Clustering is often used to explore patterns in georeferenced time series (GTS). Most clustering studies, however, only analyze GTS from one or two dimension(s) and are not capable of the simultaneous analysis of the data from three dimensions: spatial, temporal, and any third (e.g., attribute) dimension. Here we develop a novel clustering algorithm called the Bregman cuboid average triclustering algorithm with I-divergence (BCAT_I), which enables the complete partitional analysis of 3D GTS. BCAT_I simultaneously groups the data along its dimensions to form regular triclusters. These triclusters are subsequently refined using k means to fully capture spatiotemporal patterns in the data. By applying BCAT_I to time series of daily average temperature in The Netherlands (twenty-eight weather stations from 1992 to 2011), we identified the refined triclusters with similar temperature values along the spatial dimension (weather stations that represent locations) and two nested temporal dimensions (year and day). Geovisualization techniques were then used to display the patterns of intra-annual variability in temperature. Our results show that in the last two thirds of the study period, there is an intense variability of spring and winter temperatures in the northeast and center of The Netherlands. For the same period, an intense variability of spring temperatures is also visible in the southeast of the country. Our results also show that summer temperatures are homogenous across the country for most of the study period. This particular application demonstrates that BCAT_I enables a complete analysis of 3D GTS and, as such, it contributes to a better understanding of complex patterns in spatiotemporal data.

AB - Clustering is often used to explore patterns in georeferenced time series (GTS). Most clustering studies, however, only analyze GTS from one or two dimension(s) and are not capable of the simultaneous analysis of the data from three dimensions: spatial, temporal, and any third (e.g., attribute) dimension. Here we develop a novel clustering algorithm called the Bregman cuboid average triclustering algorithm with I-divergence (BCAT_I), which enables the complete partitional analysis of 3D GTS. BCAT_I simultaneously groups the data along its dimensions to form regular triclusters. These triclusters are subsequently refined using k means to fully capture spatiotemporal patterns in the data. By applying BCAT_I to time series of daily average temperature in The Netherlands (twenty-eight weather stations from 1992 to 2011), we identified the refined triclusters with similar temperature values along the spatial dimension (weather stations that represent locations) and two nested temporal dimensions (year and day). Geovisualization techniques were then used to display the patterns of intra-annual variability in temperature. Our results show that in the last two thirds of the study period, there is an intense variability of spring and winter temperatures in the northeast and center of The Netherlands. For the same period, an intense variability of spring temperatures is also visible in the southeast of the country. Our results also show that summer temperatures are homogenous across the country for most of the study period. This particular application demonstrates that BCAT_I enables a complete analysis of 3D GTS and, as such, it contributes to a better understanding of complex patterns in spatiotemporal data.

KW - ITC-ISI-JOURNAL-ARTICLE

UR - http://www.scopus.com/inward/record.url?scp=85021051104&partnerID=8YFLogxK

UR - https://ezproxy2.utwente.nl/login?url=http://dx.doi.org/10.1080/24694452.2017.1325725

U2 - 10.1080/24694452.2017.1325725

DO - 10.1080/24694452.2017.1325725

M3 - Article

VL - 108

SP - 71

EP - 87

JO - Annals of the American Association of Geographers

JF - Annals of the American Association of Geographers

SN - 2469-4452

IS - 1

ER -